Repositori institucional URV
| URV's Author/s: | Moreno Ribas, Antonio |
| Author, as appears in the article.: | Jabreel, Mohammed; Moreno, Antonio |
| Author's mail: | antonio.moreno@urv.cat |
| Author identifier: | 0000-0003-3945-2314 |
| Journal publication year: | 2019 |
| Publication Type: | Journal Publications |
| ISSN: | 20763417 |
| e-ISSN: | 2076-3417 |
| APA: | Jabreel, Mohammed; Moreno, Antonio (2019). A deep learning-based approach for multi-label emotion classification in Tweets. Applied Sciences-Basel, 9(6), 1123-1123. DOI: 10.3390/app9061123 |
| Papper original source: | Applied Sciences-Basel. 9 (6): 1123-1123 |
| Abstract: | © 2019 by the authors. Currently, people use online social media such as Twitter or Facebook to share their emotions and thoughts. Detecting and analyzing the emotions expressed in social media content benefits many applications in commerce, public health, social welfare, etc. Most previous work on sentiment and emotion analysis has only focused on single-label classification and ignored the co-existence of multiple emotion labels in one instance. This paper describes the development of a novel deep learning-based system that addresses the multiple emotion classification problem in Twitter. We propose a novel method to transform it to a binary classification problem and exploit a deep learning approach to solve the transformed problem. Our system outperforms the state-of-the-art systems, achieving an accuracy score of 0.59 on the challenging SemEval2018 Task 1:E-cmulti-label emotion classification problem. |
| Article's DOI: | 10.3390/app9061123 |
| Link to the original source: | https://www.mdpi.com/2076-3417/9/6/1123 |
| Papper version: | info:eu-repo/semantics/publishedVersion |
| licence for use: | https://creativecommons.org/licenses/by/3.0/es/ |
| Department: | Enginyeria Informàtica i Matemàtiques |
| Licence document URL: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Thematic Areas: | Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física |
| Keywords: | Twitter Sentiment analysis Opinion mining Emotion classification Deep learning |
| Entity: | Universitat Rovira i Virgili |
| Record's date: | 2024-10-12 |
| Journal volume: | 9 |
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| DocumentPrincipal | DocumentPrincipal | application/pdf |
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